Overview

PART 2 : DATA DELIVERY

DATA: sav file

TIDY DATA: csv file

CODE BOOK: knitted html

Generating a tidy dataset

importing data, converting to csv

path = "./../../data/PPD_hairCortisol_PlosOne_.sav"

dataset= read_sav(path) #meta data dropped? 

write.csv(dataset, file = "./../../data/data_span.csv")

Programatic Translation

The first step we took to produce a tidy dataset was to translate the variable names from english to spanish, to do this, we first attempted a programatic method; Using the google translate API, each variable name was attempted to translate from Spanish to English.

import googletrans as gt 
import pandas as pd
data = pd.read_csv('./../../data/data_span.csv',encoding = "ISO-8859-1")
trans = gt.Translator()
with open("./../../data/data_programatic_translation.csv", 'w') as f: 
  f.write('original, translated\n')
  for old_col in data.columns.values:  
    new_col = trans.translate(old_col, src = 'spanish', dest='en').text
    f.write(old_col + ',' + new_col + '\n')
    
    data.rename(columns = {old_col:new_col}, inplace = True)
  
data.to_csv( "./../../data/data_eng.csv" )

This produces two files, first, a new dataset csv with the header names translated (attempted) and two, a csv documenting the original variable name and the translated variable name.

english csv
translation mapping

VARIABLES TO INCLUDE

These listed variables are those required to reproduce the paper plots or extend the study.

Paper Variables

Paper Variables

Data Cleaning

We will try to manually review, translate and generate a codebook (Good tutorial) in the following steps.

ALL of our raw data are numerics, there is codebook mapping categorical -> numeric in some of the variables if the .sav file is interograted from SPSS. We have tried to include as much of this information as possible, but the meta data was dropped when the sav file is imported into R, so much was lost.

Summary: dropped na rows (29), renamed variables and added attribute ‘Description’ which can be found in the summary statistics tab of our codebook.

df = read.csv("./../../data/data_eng.csv", header = TRUE)
#df %>% variable.names()

# THIS IS WHERE WE CAN DROP UNUNSED VARIABLES, MUTATE NAMES AND ADD META DATA DESCRIBING VARIABLES (variable attribute data)

# There are 29 rows of all NA variables that were brought in from SPSS empty rows 
before <- df %>% nrow() 
df <- df %>% na.omit() 
after <- df %>% nrow() 
print(paste('rows dropped:', after-before))
## [1] "rows dropped: -29"
df <- df %>% mutate('age' = Age)
attr(df$age, "Description") <- "The age of the mother"

df <- df %>% mutate("nationality" = NationalityDicotomica)
attr(df$NationalityDicotomica, 'Description') <- "The patient's nationality or country of origin at the time of pregnancy"

#df <- df %>% mutate('Marital_Status' = ) # CANT FIND THIS ONE

df <- df %>% mutate('employed' = EmploymentSituationDico)
attr(df$employed, 'Description') <- "Employment situation; is or is not employed at time of pregnancy"

df <- df %>% mutate('occupation' = Profession)
attr(df$occupation, "Description") <- 'If employed, what was the mothers occupation at the time of pregnancy' 

df <- df %>% mutate("education_level" = Level_Studies) 
attr(df$education_level, 'Description') <- "The highest level of education that the mother had received at the time of pregnancy/study"

df <- df %>% mutate('sport' = Sport)
attr(df$sport, 'Description') <- 'tracking the physical activity of the mother; whether the mother played a sport (during pregnancy) [y/n]' # ???

df <- df %>% mutate('pet' = PetDicotomica)
attr(df$pet, 'Description') <- 'designation for mothers with pets at time of pregnancy' 

df <- df %>% mutate('dyed_hair' = Appearance)
attr(df$dyed_hair, "Description") <- 'designation for mothers with dyed hair during pregnancy'  # I THINK this is right, need to check

df <- df %>% mutate('first_pregnancy' = FirstPregnancy)
attr(df$first_pregnancy, 'Description') <- 'primiparous; designation for mothers who are having a child for the first time'

df <- df %>% mutate('wanted_pregnancy' = WantedPregnancyDico)
attr(df$wanted_pregnancy, 'Description') <- 'Designation for mothers who desired the pregnancy (planned?)'


df <- df %>% mutate('pregnancy_method' = PregnancyMethodDico)
attr(df$pregnancy_method, 'Description') <- 'designation for  *spontaneous* method of fertilization and *Fertility Treatment* or artificial insemination' # PC term? also, lol on "spontaneous" 

df <- df %>% mutate('previous_miscarriage' = PreviousMiscarriagesDico)
attr(df$previous_miscarriage, 'Description') <- 'Designation for mothers who have had a miscarriage prior to the current pregnancy' 

# df <- df %>% mutate('delivery' = ) # CANT FIND DELIVERY AND LABOR

#df <- mutate('delivery_antisthesia' = )    # Not sure what this one is 

df <- df %>% mutate('fetus_sex' = SexFetalDico)
attr(df$fetus_sex, "Description") <- 'the sex of the fetus' # numeric mapping ???  

# -------- DEPRESSION METRICS 

df <- df %>% mutate('postpartum_depression' = depreposparto)
attr(df$postpartum_depression, 'Description') <- "Diagnosis of post-partum depression in the mother" ### ???? more?


df <- df %>% mutate('depression_tri1' = DEPRESSION1)
df <- df %>% mutate('depression_tri2' = DEPRESSION2)
df <- df %>% mutate('depression_tri3' = DEPRESSION3)
attr(df$depression_tri1, 'Description') <- 'antenatal depression during the 1st trimester.'
attr(df$depression_tri2, 'Description') <- 'antenatal depression during the 2nd trimester.'
attr(df$depression_tri3, 'Description') <- 'antenatal depression during the 3rd trimester.'

df <- df %>% mutate('epds' = EPDS)
attr(df$epds, 'Description') <- 'continuous metric of postnatal depression'  #measurement name, type? 

# --------- CORTISOL metrics 

df <- df %>% mutate('cortisol_tri1' = Cortisol1) #WHAT IS LNCORTISOL1 ????? - Two metrics for coritsol??
df <- df %>% mutate('cortisol_tri2' = Cortisol2)
df <- df %>% mutate('cortisol_tri3' = Cortisol3)
attr(df$cortisol_tri1, 'Description') <- 'Cortisol levels in mothers during pregnancy during the 1st trimester' # units??? look into lncortisol variable, which one should we be using? 
attr(df$cortisol_tri2, 'Description') <- 'Cortisol levels in mothers during pregnancy during the 2nd trimester'
attr(df$cortisol_tri3, 'Description') <- 'Cortisol levels in mothers during pregnancy during the 3rd trimester'

# ---------- SELECT 

df_tidy <- df %>% select(age, education_level, nationality, postpartum_depression, employed, occupation, sport, sport, pet, dyed_hair, first_pregnancy, wanted_pregnancy, pregnancy_method, previous_miscarriage, fetus_sex, epds, depression_tri1, depression_tri2, depression_tri3, cortisol_tri1, cortisol_tri2, cortisol_tri3 )

#glimpse(df_tidy)

write.csv(df_tidy, file='./../../data/tidy_data.csv') # this might drop Description attribute 
save(df_tidy, file='./../../data/tidy_data.Rdata') # this way we can always load it back as is.

Tidy Data

The tidy dataset can be found in the data directory, linked here

Producing Codebook

Adding information attribute to our tidy data to map numerics to categoricals

numeric_mapping <- 'Primer embarazo / First Pregnancy:
0 = No
1 = Yes

Tipo Embarazo / Pregnancy Method:
1 = spontaneously and normally
2 = in vitro fertilization
3 = artificial insemination

Nivel Estudios / level of education
1 = primary
2 = secondary
3 = university students
4 = without studies

Laboral / employee situation
1 = unemployed
2 = full-time job
3 = half-day job
4 = student
5 = work and study

Aspecto Pelo/ Hair Appearance
0 = dyed
1 = natural

Deporte / Sport
0 = No
1 = Yes

Profesión / Profession
1 = unemployment
2 = Head of nurse
3 = Doctor
4 = Nurse
5 = housewife
6 = teacher
7 = management
8 = dental clinic
9 = banking
10 = untrained
11 = psychologist
12 = engineer

Origen / Nationality
1 = Spain
2 = Argentina
3 = Chile
4 = Morocco
5 = Romania
6 = Germany
7 = Russia
8 = Ecuador
9 = Peru
10 = Paraguay

depreposparto / depress post partum
1 = sindepre?
2 = condepre?'

attr(df_tidy, 'numeric mapping information') <- numeric_mapping

Generate a knitted codebook

knitr::opts_chunk$set(warning = TRUE, message = TRUE, echo = FALSE) #this has to go before the codebook() call, make sure echo=FALSE is included. 
my_codebook <- codebook(df_tidy) #my_codebook # (TAKES FOREVER TO KNIT) This will produce a codebook, but we need to have this knitted in it's own html file and not echo the code chunks... 
## Warning in codebook(df_tidy): The variables session, created, ended have
## to be defined for automatic survey repetition detection to work. Set to no
## repetition by default.
## No missings.
knitr::opts_chunk$set(warning = TRUE, message = TRUE, echo = TRUE)

fh<-file("tmp.Rmd")
writeLines(my_codebook, fh)
close(fh)

render(input='tmp.Rmd', output_file = 'group7_codebook.html', output_dir = getwd() )
## 
## 
## processing file: tmp.Rmd
## 
  |                                                                       
  |                                                                 |   0%
  |                                                                       
  |.................................................................| 100%
##   ordinary text without R code
## output file: tmp.knit.md
## "C:/Program Files/RStudio/bin/pandoc/pandoc" +RTS -K512m -RTS tmp.utf8.md --to html4 --from markdown+autolink_bare_uris+ascii_identifiers+tex_math_single_backslash --output pandoc102479f27cc5.html --smart --email-obfuscation none --self-contained --standalone --section-divs --template "C:\R-3.5.1\library\rmarkdown\rmd\h\default.html" --no-highlight --variable highlightjs=1 --variable "theme:bootstrap" --include-in-header "C:\Users\natha\AppData\Local\Temp\RtmpqE23hE\rmarkdown-str102417db1a45.html" --mathjax --variable "mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"
## 
## Output created: group7_codebook.html

The codebook is a knitted html file in the P2 folder and is linked here

The dataframe df_tidy has an attribute “Description” that can be accessed with fn: attribute(df$variable, “Description”)

REFERENCES

[1] codebook package:

Preprint Arslan, R. C. (2018). How to automatically generate rich codebooks from study metadata. doi:10.31234/osf.io/5qc6h

Zenodo Arslan, R. C. (2018). Automatic codebooks from survey metadata (2018). URL https://github.com/rubenarslan/codebook. DOI